README edits

README.md

@@ -1,8 +1,13 @@
 Learning convex optimization control policies
 =============================================
 
-This repository accompanies the paper [Learning convex optimization control policies](http://web.stanford.edu/~boyd/papers/pdf/learning_cocps.pdf). It contains the source code for the examples therein.
+This repository accompanies the paper [Learning convex optimization control policies](http://web.stanford.edu/~boyd/papers/pdf/learning_cocps.pdf). It contains the source code for the examples therein as IPython notebooks.
 
+Our examples make use the Python package
+[cvxpylayers](https://github.com/cvxgrp/cvxpylayers) to differentiate through
+convex optimization problems.
+
+## Abstract
 Many control policies used in various applications determine the input or
 action by solving a convex optimization problem that depends on the current
 state and some parameters. These types of control policies are tuned by varying
@@ -14,7 +19,15 @@ respect to the parameters. Our procedure relies on recently developed methods
 that can efficiently evaluate the derivative of the solution of a convex
 optimization problem with respect to its parameters.
 
-This repository contains several examples of our method, in IPython notebooks.
-Our examples make use the Python package
-[cvxpylayers](https://github.com/cvxgrp/cvxpylayers) to differentiate through
-convex optimization problems.
+## Citing
+```
+@article{agrawal2019cocp,
+    author       = {Agrawal, Akshay and Barratt, Shane and Boyd, Stephen and Stellato, Bartolomeo},
+    title        = {Learning Convex Optimization Control Policies},
+    journal      = {arXiv},
+    archivePrefix = {arXiv},
+    eprint = {1912.09529},
+    primaryClass = {math.OC},
+    year    = {2019},
+}
+```